Final Project

library(tidyverse)
library(janitor)
library(knitr)
library(gee)
library(MuMIn)
library(forcats)
library(knitr)



library(RLRsim) # for testing significance of random intercept

dat = 
  read.csv(file = '../frmgham2.csv') %>%
  clean_names()

dim(dat)

glimpse(dat)
head(dat)
View(dat)

dat %>% select(sex) %>% head()

dat = 
  dat %>%
  mutate(sex = factor(sex)) #%>% fct_recode(sex, "female" =  "2", "male" = "1")

Smoking vs. Age, Sex

(1): Smoking ~ age, sex

Is there a relationship between age and smoking status?

ANS: Yes, the proportion of smokers decreases with the age.

dat %>%
  select(cursmoke, age) %>% 
  ggplot(aes(x = age, y = cursmoke)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

Does this relationship differ by sex?

ANS: There is a higher proportion of smoker among men compared to women as both age ,but there is no interaction between age and sex.

dat %>%
  select(cursmoke, age, sex) %>% 
  ggplot(aes(x = age, y = cursmoke, group = sex, color = sex)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

(2) number of sigarets ~ age, sex

Is there a relationship between the number of cigarettes smoked per day and age?

ANS: Yes, number of sigarets smoked per day stays constant for 30-50 years old and decreases with age after 50 years old.

All

dat %>%
  select(cigpday, age) %>% 
  ggplot(aes(x = age, y = cigpday)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

Smokers only

dat %>%
  select(cigpday, age) %>% 
  filter(cigpday > 0) %>%
  ggplot(aes(x = age, y = cigpday)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

Does this relationship differ by sex?

ANS: There is sex effect (men smoke higer number of sigarets per day than women across age), but there is no sex and age interaction.

All

dat %>%
  select(cigpday, age, sex) %>% 
  ggplot(aes(x = age, y = cigpday, group = sex, color = sex)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

Smokers only

dat %>%
  select(cigpday, age, sex) %>% 
  filter(cigpday > 0) %>% 
  ggplot(aes(x = age, y = cigpday, group = sex, color = sex)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

Smoking vs. health outcomes

(1) The relationship between current smoking status and systolic blood pressure.

smoking ~ sysbp

ANS: Proportion of smokers decreases with increase of systolic blood presure

dat %>%
  select(cursmoke, sysbp) %>% 
  ggplot(aes(x = sysbp, y = cursmoke)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

ANS: slightly higher sysbp for non-smokers

dat %>%
  select(cursmoke, sysbp) %>% 
  mutate(cursmoke = factor(cursmoke)) %>%
  ggplot(aes(y = sysbp, x = cursmoke)) +
  geom_boxplot(outlier.colour = "white") +
  geom_jitter(width = 0.1, alpha = 0.1) +
  theme_bw() 

smoking ~ sysbp, sex

ANS: Proportion of smokers decreases with increase of systolic blood presure; the proportion is higher for men (sex effect).

dat %>%
  select(cursmoke, sysbp, sex) %>% 
  ggplot(aes(x = sysbp, y = cursmoke, group = sex, color = sex)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

ANS: no differences in sysbp between male and female smokers and non-smokers

dat %>%
  select(cursmoke, sex, sysbp) %>% 
  mutate(cursmoke = factor(cursmoke),
           smoke_sex = interaction(cursmoke, sex)) %>% 
  ggplot(aes(y = sysbp, x = smoke_sex)) +
  geom_boxplot(outlier.colour = "white") +
  geom_jitter(width = 0.1, alpha = 0.1) +
  theme_bw() 

(2) The relationship between current smoking status and diastolic blood pressure.

smoking ~ diabp

ANS: Proportion of smokers decreases with increase of diastolic blood presure for BP=100 ad then proportion increases again (latter could be due to not enough data)

dat %>%
  select(cursmoke, diabp) %>% 
  ggplot(aes(x = diabp, y = cursmoke)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

ANS: no difference

dat %>%
  select(cursmoke, diabp) %>% 
  mutate(cursmoke = factor(cursmoke)) %>%
  ggplot(aes(y = diabp, x = cursmoke)) +
  geom_boxplot(outlier.colour = "white") +
  geom_jitter(width = 0.1, alpha = 0.1) +
  theme_bw() 

smoking ~ diabp, sex

ANS: Proportion of smokers decreases with increase of diastolic blood presure; the proprtions are higher for men (sex effect).

dat %>%
  select(cursmoke, diabp, sex) %>% 
  ggplot(aes(x = diabp, y = cursmoke, group = sex, color = sex)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

ANS: no difference

dat %>%
  select(cursmoke, sex, diabp) %>% 
  mutate(cursmoke = factor(cursmoke),
           smoke_sex = interaction(cursmoke, sex)) %>% 
  ggplot(aes(y = diabp, x = smoke_sex)) +
  geom_boxplot(outlier.colour = "white") +
  geom_jitter(width = 0.1, alpha = 0.1) +
  theme_bw() 

(3) The relationship between current smoking status and serum total cholesterol.

smoking ~ totchol

ANS: Proportion of smokers slightly decreases with increase of total cholesterol values

dat %>%
  select(cursmoke, totchol) %>% 
  ggplot(aes(x = totchol, y = cursmoke)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

ANS: no difference

dat %>%
  select(cursmoke, totchol) %>% 
  mutate(cursmoke = factor(cursmoke)) %>%
  ggplot(aes(y = totchol, x = cursmoke)) +
  geom_boxplot(outlier.colour = "white") +
  geom_jitter(width = 0.1, alpha = 0.1) +
  theme_bw() 

smoking ~ totchol, sex [!!!]

ANS: Proportion of smokers hasnonlinera relationship with total cholesterol for women; proprtions increases with increase in total cholesterol for men (sex by totchol interaction effect).

dat %>%
  select(cursmoke, totchol, sex) %>% 
  ggplot(aes(x = totchol, y = cursmoke, group = sex, color = sex)) +
  geom_jitter(height = 0.1, alpha = 0.1) +
  geom_smooth(lwd = 1.5) +
  theme_bw() 

ANS: no difference

dat %>%
  select(cursmoke, sex, totchol) %>% 
  mutate(cursmoke = factor(cursmoke),
           smoke_sex = interaction(cursmoke, sex)) %>% 
  ggplot(aes(y = totchol, x = smoke_sex)) +
  geom_boxplot(outlier.colour = "white") +
  geom_jitter(width = 0.1, alpha = 0.1) +
  theme_bw()